But ok, let's compare: https://benchlm.ai/compare/gpt-6-astra-vs-qwen3-8-flash-next
Astra's score: 85.8/100
Qwen3.8-flash-next's score: 55.87/100
So there's a ~30 pt difference between them. Let's add 5% more for quantization damage.
My perspective is, yes - that's a huge difference. No - it doesn't matter. Flash-next is above the threshold of useful intelligence. I can ask it to do something and it does it. Sure, it burns some tokens on bad ideas on the way, and sure it can't turn a single paragraph prompt into million dollar business. I don't need or want that - I want something I can ask to refactor a module, and it does it and it doesn't stop until the module compiles and all the tests pass. flash-next does that.
Don't have to be. They just have to be good enough for the tasks you need.
I use both models extensively and there is no way I would ever agree that Astra and Qwen3.8-FN are that close in the real world.
I agree with the other comments: These comparisons are silly.
You keep saying that you don’t understand why anyone would have a Claude subscription and reciting ideological reasons not to. I’m assuming this means you don’t use their new models much? That’s the only explanation I can think of for how someone could think that Opus 5.5 or even Sonnet 5.5 and Qwen3.8-FN are close.
I use them both (often Qwen3.8-FN as a subagent) and any claims that they’re actually pretty close read as downright delusional to me.
Putting aside the absurd straw man, you’re touching on an important point: these small open weight models are sufficient for how you like to work, but it’s not remotely good enough for how I like to work. Opus 5.5 is barely sufficient for how I like to work (many parallel work streams, as little of me in the loop as possible, etc).
So not really work, then?
You’re automating yourself out of a job using capital you don’t own, and you think your position will hold? I am skeptical.
I work like the parent poster does. I am working harder than before the AI tools came around. I am just working at a higher level. Not too dissimilar from someone moving from programming in assembly language to significantly higher level language.
> as little of me in the loop as possible
You may be confused about this. If I am running just one loop at a time, you'd be right. What modern tools allow me to do is think more like a supervisor that has dozens of engineering teams available to him. I always have a long list of problems I need to solve. I used to sit with one and bang on it until it would pass acceptance criteria. Sometimes that took months. Now I just farm out each task to an agent orchestrator, provide it details on what I want done, a bit of design and architecture and acceptance criteria. I then step away from it and move to the next agent orchestrator and repeat the process for the next task. I usually have 6 or 7 large tasks being worked on at the same time. This is currently my personal limit. I can't babysit more than that. But as the models and harnesses get better I suspect I will have to do less babysitting and this process will get more autonomous.
It may sound like this process would remove the need for engineers. In my organization it is doing quite the opposite. As the organization figures out that we can do a lot more, we change our plans to build more. A lot more. Building still requires engineers, more than before, for us. But only those who can think like architects and can manage large teams of AI agents and orchestrators that work 24x7.
And I work for myself, so no, not automating myself out of a job.
Although I suppose I’m automating the people I won’t hire now out of a future potential job.
I run my own local models, and I can tell that yes, one ask and they do... with many more errors, and much much slower, compared to the lowest levels of frontier models.
Sure, if one has no standards at all, local LLMs definitely are "above the threshold of useful intelligence".
I'd rather wait all day for an impeccable implementation of a single feature than get 10 features worth of "okayish" code